The present invention relates generally to communication systems and more particularly relates to an apparatus for and a method of generating soft decision information for symbols received over a channel.
In recent years, the world has witnessed explosive growth in the demand for wireless communications and it is predicted that this demand will increase in the future. There are already over 500 million users that subscribe to cellular telephone services and the number is continually increasing. Eventually, in the not too distant future the number of cellular subscribers will exceed the number of fixed line telephone installations. Already, in many cases, the revenues from mobile services already exceeds that for fixed line services even though the amount of traffic generated through mobile phones is much less than in fixed networks.
Other related wireless technologies have experienced growth similar to that of cellular. For example, cordless telephony, two way radio trunking systems, paging (one way and two way), messaging, wireless local area networks (WLANs) and wireless local loops (WLLs). In addition, new broadband communication schemes are rapidly being deployed to provide users with increased bandwidth and faster access to the Internet. Broadband services such as xDSL, short range high speed wireless connections, high rate satellite downlink (and the uplink in some cases) are being offered to users in more and more locations.
In connection with cellular services, the majority of users currently subscribe to digital cellular networks. Almost all new cellular handsets sold to customers are based on digital technology, typically second generation digital technology. Currently, third generation digital networks are being designed and tested which will be able to support data packet networks and much higher data rates. The first generation analog systems comprise the well known protocols AMPS, TACS, etc. The digital systems comprise GSM, TDMA (IS-136) or CDMA (IS-95), for example.
A diagram illustrating an example prior art communication system employing an inner and outer encoder in the transmitter, inner and outer decoding stages in the receiver and a noise source after the channel is shown in FIG. 1. The communication system, generally referenced 10, represents the typical scheme that may be used in providing many of the communication services described above. In such a system, the transmitter 11 comprises an encoder 14, symbol generator 16 and modulator 18. Input data bits 12 to be transmitted are input to the encoder 14, which may comprise an error correction encoder such as a Reed Solomon encoder, a convolutional encoder, a parity bit generator, etc. The encoder functions to add redundancy bits to enable errors in transmission to be located and corrected.
It is noted that both the inner and outer decoders in the receiver have complementary encoders in the transmitter. The outer encoder in the transmitter comprises the encoder 14, e.g., Reed Solomon, etc. The inner encoder comprises the channel 20 which often times can be modeled as an L-symbol long FIR-type channel.
The bits output of the encoder are then mapped to symbols by the symbol generator 16. The symbol generator functions to transform the bits to modulator symbols. For example, an 8-PSK modulator converts input bits into one of eight symbols. Thus, the symbol generator generates a symbol for every three input bits.
The output from the mapper is input to the modulator which receives symbols in the M-ary alphabet and generates the analog signal subsequently transmitted over the channel 20. The channel may comprise a mobile wireless channel, e.g., cellular, cordless, a fixed wireless channel, e.g., satellite, or may comprise a wired channel, e.g., xDSL, ISDN, Ethernet, etc. The processing performed in the transmitter is intended to generate a signal that can be transmitted over the channel to provide robust, error free detection by the receiver.
At the receiver 13, the analog signal from the channel is input to front end circuitry 22 which demodulates and samples the received signal to generate received samples y(k) 21. The samples are first input to an inner decoder 24. An example of an inner decoder is an equalizer which compensates for the ISI caused by the delay and time spreading of the channel. Examples of commonly used types of equalizers include the maximum likelihood sequence estimation (MLSE) equalizer that utilizes the well known Viterbi Algorithm (VA), linear equalizer and decision feedback equalizer (DFE). The function of the equalizer is to detect the symbols that were originally transmitted by the modulator.
The output of the inner decoder comprises symbols s(k) 23 which represent hard decisions. The hard decisions are then input to an outer decoder 26 which functions to locate and fix errors using the redundancy inserted by the encoder. The outer decoder generates the binary receive data.
An example of an outer decoder is a convolutional decoder that utilizes the Viterbi Algorithm. The Viterbi algorithm is widely used in communication systems and has been adapted to perform functions including demodulation, decoding, equalization, etc. Many systems utilize the Viterbi Algorithm in both the inner and outer decoding stages.
As described above, the outer decoder, in some systems, is adapted to utilize the hard decisions output from the inner decoder, e.g., the equalizer. Optimal decoders, on the other hand, require soft decision inputs. For example, an outer decoder that utilizes the Viterbi Algorithm to perform convolutional forward error correction decoding, requires soft decisions as input. The advantage of a Viterbi decoder is that it can efficiently process soft decision information.
One drawback of such a system is that in some cases the outer decoder is very sensitive to error bursts produced by the inner decoder. A second drawback arises when the inner decoder cannot provide soft decision information and thus only provides hard decisions to the outer decoder. In such a system, the performance of the outer stages of a concatenated coding system is substantially lower than that of a system wherein the inner decoder is capable of generating soft decision information.
The problem is illustrated by considering a receiver adapted to handle a GSM or GERAN signal. Such a system utilizes convolutional coding for performing Forward Error Correction (FEC) over channels that require equalization. The equalizer and outer FEC decoder typically used employ the Viterbi Algorithm in their operation. The output of the equalizer, however, only produces hard decisions which leads to reduced performance of the outer VA convolutional FEC decoder.
There exist a class of decoders that provide improved performance by utilizing soft information about the received symbols rather than only hard decisions. Examples include turbo decoders and soft decision convolutional decoders utilizing the Viterbi Algorithm, etc. This class of decoders provides better performance by taking into account soft information about the reliability of received symbols. The improved performance of the decoder cannot be realized, however, when soft information about the received symbols is not available.
The problem of error bursts can be eliminated by the use of interleaving in the transmitter and de-interleaving in the receiver, between the inner and the output decoders. The second problem can only be eliminated by providing an inner decoder capable of generating soft decision information.
Several prior techniques have been developed to provide soft symbol decisions from the inner decoder (or equalizer) that can be used by a soft decoder. Most of these soft output equalizer techniques are based on maximum likelihood sequence estimation (MLSE) or computational complex methods such as maximum a posteriori (MAP) algorithms. A few of these techniques are described below.
One technique uses an optimum soft output algorithm derived under the constraint of fixed decision delay for detection of M-ary signals in the presence of ISI and additive white Gaussian noise, as described in xe2x80x9cOptimum Soft-Output Detection for Channels with Intersymnbol Interference,xe2x80x9d Y. Li, B. Vucetic, Y. Sato, IEEE Trans. on Inform. Theory, Vol. 41, No. 3, May 1995. This technique is a type of symbol by symbol maximum a posteriori (MAP) probability algorithm which functions to reduce the memory and computation requirements of the MAP algorithm. MAP algorithm techniques typically suffer from high computational complexity. With this algorithm, however, the memory requirement increases linearly with the sequence length which makes it unsuitable for long sequences. A suboptimum soft-output algorithm (SSA) is described which is an extension of the Viterbi Algorithm with the additional requirements of storing a soft survivor for each state and updating it recursively. This suboptimal algorithm sacrifices a modest amount of performance in return for reduced complexity. Its complexity, however is still higher than that of the VA.
Another extension of the Viterbi Algorithm is described in xe2x80x9cA Viterbi Algorithm with Soft-Decision Outputs and its Applications,xe2x80x9d J. Hagenauer and P. Hoeher, Proc. GLOBCOM ""89 (Dallas, Tex.), Nov. 1989, Vol. 3, pages 47.1.1-47.1.7. In addition to the most likely path sequence, the algorithm provides either a posteriori probability of each symbol or a reliability value. From this, the soft decisions are generated to be used in decoding of outer codes. The inner soft output Viterbi Algorithm (SOVA) accepts and delivers soft sample values. A disadvantage of this algorithm is that it operates on bits rather than symbols.
A SOVA algorithm for non-binary codes that is equivalent to the Max-Log MAP algorithm has been developed and described in xe2x80x9cOn SOVA for Nonbinary Codes,xe2x80x9d L. Cong, W. Xiaofu and Y. Xiaoxin, IEEE Comm. Letters, Vol. 3, No. 12, December 1999. Viterbi-type recursions are used to update the reliability measures associated with the SOVA. This algorithm, however, is complex and requires significant computing resources to implement.
The results of a study of the design and performance of soft output adaptive equalization techniques based on suboptimum trellis based soft output decoding algorithm is given in xe2x80x9cTrellis-Based Soft-Output Adaptive Equalization Techniques for TDMA Cellular Systems,xe2x80x9d J. I. Park, S. B. Wicker and H. L. Owen, IEEE Trans. Vehicular Technology, Vol. 49, No. 1, January 2000. The study compares the performance of the soft output Viterbi algorithm (SOVA, suboptimum soft-output algorithm (SSA) and the modified maximum a posteriori (MAP) algorithm within the context of a TDMA cellular system using Gaussian minimum shift keying (GMSK) modulation. Soft output information is generated from the inner equalizer and used in a soft decision decoder. The trellis based soft output decoding algorithms including SOVA, SSA and modified MAP can be used to estimate reliability information.
In addition, U.S. Pat. No. 5,457,704, issued to Hoeher et al., describes a method for providing reliability information for each decoded data symbol at the output of an arbitrary decoder. The reliability information is generated by comparing the decoded sequence to a small list of alternative sequences which differ at least in the symbol for which the reliability is being sought.
Several disadvantages of one or more of the above described prior art techniques are that they (1) have a high level of computational complexity and are difficult and impractical to implement in communication systems having a large symbol alphabet, (2) have a high level of computational complexity and are impractical to implement in communication systems wherein the channel has a time spread of several symbol periods, (3) do not generate soft output information on a symbol basis, (4) do not provide soft output information for all symbol possibilities for use by an outer decoding stage (i.e. they do not provide a complete information packet), or (5) are restricted to Viterbi decoding in the inner decoding state.
Accordingly, the present invention provides a novel and useful apparatus for and method of generating soft decision information from a sequence of hard symbol decisions output from a decoder in a communications receiver that overcomes the disadvantages of the prior art. The present invention is suitable for use with a wide range of communication systems and is particularly useful in receivers that comprise concatenated coding schemes whereby the output of an inner decoder is subsequently processed by an outer decoder. The outer decoder is a soft decision decoder whose performance is optimized when soft decision values are available.
Thus, the present invention has applications when the inner decoder used in the communications receiver is only able to generate hard decisions and does not have the capability of generating soft output values. The soft output generator of the present invention is adapted to generate soft decision information from the hard symbol decisions output of the inner decoder. The soft decision output can then be applied to the soft outer decoder thus realizing maximum performance therefrom. The output of the soft decision generator comprises a full information packet, i.e. a soft decision value for each symbol possibility. Alternatively, the soft decision generator may be adapted to output soft values for less than the full number of symbol possibilities.
In addition, the invention is applicable to concatenated communication receivers in which use of a reduced complexity soft output equalizer is dictated by system requirements. This may be the case in (1) communication systems having relatively large size symbol alphabets or in (2) systems where the channel is characterized by a time spread of several symbol periods which is the case in most mobile radio channels.
The present invention provides an apparatus for and method of computing soft symbol decisions given (1) hard symbol decisions from the equalizer, (2) channel model information (e.g., FIR filter taps used to estimate the channel), and (3) the input samples received from the channel. The log likelihood ratio (LLR) for a symbol is derived by determining the conditional probability of the input sample sequence given the hard symbol decision sequence.
A key feature of the invention is that the noise variance is used in computing the soft output values, thus resulting in better overall performance of the receiver. In many real world systems, the noise statistic varies with time and cannot be assumed constant. In accordance with another key feature of the invention, soft output values are generated for all symbol possibilities. Thus, considering 8-PSK modulation, the invention is operative to generate eight soft output values for each symbol.
Another feature of the invention is that it is operative to generate a complete information packet or a portion thereof which can subsequently be used by the outer decoder. It is noted that the invention achieves this using a relatively low complexity technique described infra.
The invention can be implemented in either hardware or software. In one embodiment, symbol vectors are calculated beforehand and stored in a table. An error vector calculator functions to calculate an error vector that is applied to soft output calculation units. Each soft output calculation unit functions to generate a soft output value for a particular symbol possibility.
In another embodiment, a computer comprising a processor, memory, etc. is operative to execute software adapted to perform the soft output generator method of the present invention.
The present invention provides the following advantages and benefits. The generation of soft decision information is independent of the type of equalizer used in the receiver. This permits soft value information to be generated in a receiver that incorporates a Low Complexity Equalizer as well as in one that incorporates a Full Complexity Maximum Likelihood Equalizer. The soft output generator method is computationally efficient in that a minimum number of arithmetic operations are required for its implementation.
Another benefit is that the soft output generator method does not require any additional memory, unlike many prior art soft output techniques. In addition, the method performs relatively well for good to moderate Symbol Error Rate (SER) values of the inner decoder.
There is thus provided in accordance with the present invention, a method of generating soft decision information for an M-ary symbol in a communication receiver coupled to a channel wherein the communication receiver includes a first decoder adapted to generate a sequence of hard symbol decisions from a received signal, the method comprising the steps of providing the sequence of hard symbol decisions from the first decoder, providing a plurality of input samples generated in accordance with the received signal, providing an estimate of the channel, generating a soft decision value for one or more possible values of the M-ary symbol for each given symbol wherein each soft decision value is calculated as a function of the sequence of hard symbol decisions, the plurality of input samples and the channel estimate.
There is also provided in accordance with the present invention, a communications receiver for receiving and decoding an M-ary transmitted signal, comprising a radio frequency (RF) front end circuit for receiving and converting the M-ary transmitted signal to a baseband signal, a demodulator adapted to receive the baseband signal and to generate a received signal therefrom in accordance with the M-ary modulation scheme used to generate the transmitted signal, a first decoder operative to receive the received signal and to generate a sequence of hard symbol decisions therefrom, a soft output generator comprising processing means programmed to receive the sequence of hard symbol decisions from the first decoder, receive a plurality of input samples generated based on the received signal, receive an estimate of the channel, generate a soft decision value for one or more possible values of the M-ary symbol for each given symbol wherein each soft decision value is calculated as a function of the sequence of hard symbol decisions, the plurality of input samples and the channel estimate, and a second decoder adapted to receive the soft output values and to generate binary received data therefrom.
There is further provided in accordance with the present invention, an apparatus for generating soft decision information for an M-ary symbol in a communication receiver coupled to a channel wherein the communication receiver includes a first decoder adapted to generate a sequence of hard symbol decisions from input samples generated from a received signal, comprising error calculator means operative to generate an error vector at each symbol time as a function of the input samples, an estimate of the channel and the sequence of hard symbol decisions, a symbol vector table comprising a plurality of symbol vectors generated for all possible symbol values, and soft output calculation means adapted to generate a soft output value for each symbol possibility as a function of the error vector and the plurality of symbol vectors.
There is also provided in accordance with the present invention, a method of generating soft decision information for an M-ary symbol from a sequence of hard symbol decisions generated by a decoder, wherein the decoder adapted to receive input samples from a channel coupled thereto, the method comprising the steps of providing a plurality of input samples generated in accordance with a received signal, providing an estimate of the channel, generating an error vector at each symbol time as a function of the input samples, the channel estimate and the sequence of hard symbol decisions, providing a symbol vector table comprising a plurality of symbol vectors generated for all possible symbol values, and generating a soft output value for each symbol possibility as a function of the error vector and the plurality of symbol vectors.
There is still further provided in accordance with the present invention, a method of generating soft decision information for an M-ary symbol, the method comprising the steps of providing a sequence of error vectors ek generated by an inner decoder as a function of a plurality of input samples generated in accordance with a signal received from a channel coupled thereto, an estimate of the channel, and a sequence of hard symbol decisions also generated by the inner decoder, providing a symbol vector table comprising a plurality of symbol vectors generated for all possible symbol values, and generating a soft output value for each symbol possibility as a function of the error vector and the plurality of symbol vectors.
In addition, there is provided in accordance with the present invention, a computer readable storage medium having a computer program embodied thereon for causing a suitably programmed system to generate soft output values by performing the following steps when such program is executed on the system: receiving the sequence of hard symbol decisions from the first decoder, receiving a plurality of input samples generated based on the received signal, receiving an estimate of the channel, generating a soft decision value for all possible values of the M-ary symbol for each given symbol wherein each soft decision value is calculated as a function of the sequence of hard symbol decisions, the plurality of input samples, the channel estimate and the noise variance value.